The present disclosure relates generally to simulation and more particularly to composite simulation modeling and analysis.
Making good policy, planning, and investment decisions requires not just the gathering, mining, statistical analysis, and visualization of data, but also the use of simulation models that can predict future system behaviors. This is to help analyze the potential impacts of alternative decisions on future outcomes. Such modeling and analysis is very challenging, because high-level decisions frequently require understanding complex interactions of diverse systems across a great many domains and disciplines.
High-level health decisions, for example, can require understanding of interactions involving factors both inside and outside of healthcare, such as genetics, behavior, environment, government policy, education and even international trade. No single dataset, model, or knowledgebase system can capture all facets of such complex “systems of systems”, and there is a need for experts across widely different domains to combine their data and models.
Composing simulation models is extremely hard, because domain experts have different worldviews, use different vocabularies, sit in different organizations, and have often invested considerable effort in developing and implementing their models using different programming paradigms and development platforms. Prior approaches to simulation model composition include writing a single monolithic model, creating component models that are then compiled together, adopting common standards and interfaces, or deploying distributed-simulation frameworks in which custom communication logic is added to existing models, which are then run in a tightly synchronized manner.
All of these prior approaches have drawbacks that hinder cross-disciplinary collaboration. Monolithic models are usually difficult and expensive to build, verify, validate, and maintain, and require fine grained collaboration across disciplines and organizations. Both traditional component modeling and distributed simulation approaches typically require extensive re-coding of existing models. Requiring the use of common standards across heterogeneous scientific and engineering disciplines is unrealistic in practice.